Upload create_handler.ipynb

create_handler.ipynb

@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup & Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting requirements.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile requirements.txt\n",
+    "diffusers==0.2.4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Create Custom Handler for Inference Endpoints\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if device.type != 'cuda':\n",
+    "    raise ValueError(\"need to run on GPU\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting handler.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile handler.py\n",
+    "from typing import  Dict, List, Any\n",
+    "import torch\n",
+    "from torch import autocast\n",
+    "from diffusers import StableDiffusionPipeline\n",
+    "import base64\n",
+    "from io import BytesIO\n",
+    "\n",
+    "\n",
+    "# set device\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "\n",
+    "if device.type != 'cuda':\n",
+    "    raise ValueError(\"need to run on GPU\")\n",
+    "\n",
+    "class EndpointHandler():\n",
+    "    def __init__(self, path=\"\"):\n",
+    "        # load the optimized model\n",
+    "        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)\n",
+    "        self.pipe = self.pipe.to(device)\n",
+    "\n",
+    "\n",
+    "    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            data (:obj:):\n",
+    "                includes the input data and the parameters for the inference.\n",
+    "        Return:\n",
+    "            A :obj:`dict`:. base64 encoded image\n",
+    "        \"\"\"\n",
+    "        inputs = data.pop(\"inputs\", data)\n",
+    "        \n",
+    "        # run inference pipeline\n",
+    "        with autocast(device.type):\n",
+    "            image = self.pipe(inputs, guidance_scale=7.5)[\"sample\"][0]  \n",
+    "            \n",
+    "        # encode image as base 64\n",
+    "        buffered = BytesIO()\n",
+    "        image.save(buffered, format=\"JPEG\")\n",
+    "        img_str = base64.b64encode(buffered.getvalue())\n",
+    "\n",
+    "        # postprocess the prediction\n",
+    "        return {\"image\": img_str.decode()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test custom pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.11.0+cu113'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from handler import EndpointHandler\n",
+    "\n",
+    "# init handler\n",
+    "my_handler = EndpointHandler(path=\".\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "376de150f16b4b4bb0c3ab8c513de5c0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import base64\n",
+    "from PIL import Image\n",
+    "from io import BytesIO\n",
+    "import json\n",
+    "\n",
+    "# helper decoder\n",
+    "def decode_base64_image(image_string):\n",
+    "  base64_image = base64.b64decode(image_string)\n",
+    "  buffer = BytesIO(base64_image)\n",
+    "  return  Image.open(buffer)\n",
+    "\n",
+    "# prepare sample payload\n",
+    "request = {\"inputs\": \"a high resulotion image of a macbook\"}\n",
+    "\n",
+    "# test the handler\n",
+    "pred = my_handler(request)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "decode_base64_image(pred[\"image\"]).save(\"sample.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![test](sample.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.13 ('dev': conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "f6dd96c16031089903d5a31ec148b80aeb0d39c32affb1a1080393235fbfa2fc"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}